Automated driving systems and control logic for host vehicle velocity estimation using wide aperture radar

ABSTRACT

Presented are target object detection systems for deriving host vehicle velocities, methods for making/using such systems, and motor vehicles with host vehicle velocity estimation capabilities. A method of automating operation of vehicles includes an electronic transmitter of a vehicle&#39;s target object detection system emitting electromagnetic signals, and an electronic receiver receiving multiple reflection echoes caused by each electromagnetic signal reflecting off target objects within proximity of the vehicle. A vehicle controller determines a relative velocity vector for each target object based on these reflection echoes. The relative velocity vectors are assigned to discrete vector clusters. The controller estimates a host vehicle velocity vector as an average of the relative velocity vectors in the vector cluster containing the most relative velocity vectors and having the largest spatial spread. The controller commands one or more vehicle systems to execute one or more control operations responsive to the host vehicle velocity vector.

INTRODUCTION

The present disclosure relates generally to motor vehicles withautomated driving capabilities. More specifically, aspects of thisdisclosure relate to onboard sensing systems and adaptive logic forderiving host vehicle speed and trajectory.

Current production motor vehicles, such as the modern-day automobile,are originally equipped with or retrofit to include a network of onboardelectronic devices that provide automated driving capabilities that helpto minimize driver effort. In automotive applications, for example, themost recognizable type of automated driving feature is the cruisecontrol system. Cruise control allows a vehicle operator to set aparticular vehicle speed and have the onboard vehicle computer systemmaintain that speed without the driver operating the accelerator orbrake pedals. Next-generation Adaptive Cruise Control (ACC) is anautomated driving feature that regulates vehicle speed whileconcomitantly managing fore and aft spacing between the host vehicle andleading/trailing vehicles. Another type of automated driving feature isthe Collision Avoidance System (CAS), which detects imminent collisionconditions and provides a warning to the driver while also takingpreventative action autonomously, e.g., by steering or braking withoutdriver input. Intelligent Parking Assist Systems (IPAS), Lane Monitoringand Automated Steering (“Auto Steer”) Systems, and other Advanced DriverAssistance Systems (ADAS), along with autonomous driving features, arealso available on many modern-day automobiles.

As vehicle processing, communication, and sensing capabilities continueto improve, manufacturers will persist in offering more system-automateddriving capabilities with the aspiration of eventually producing fullyautonomous vehicles competent to operate among heterogeneous vehicletypes in both urban and rural scenarios. Original equipmentmanufacturers (OEM) are moving towards vehicle-to-vehicle (V2V) andvehicle-to-infrastructure (V2I) “talking” cars with higher-level drivingautomation that employ autonomous control systems to enable vehiclerouting with steering, lane changing, scenario planning, etc. Automatedroute generation systems utilize vehicle state and dynamics sensors, mapand road condition data, and path prediction algorithms to provide pathderivation with automated lane center and lane change forecasting.Computer-assisted rerouting techniques offer predicted alternativetravel routes that may be updated, for example, based on real-time andvirtual vehicle data.

Many automobiles are now equipped with onboard vehicle navigationsystems that utilize a global positioning system (GPS) transceiver incooperation with navigation software and geolocation mapping services toobtain roadway topography, traffic, and speed limit informationassociated with the vehicle's current location. Autonomous driving andadvanced driver assistance systems are often able to adapt certainautomated driving maneuvers based on roadway information obtained by thein-vehicle navigation system. Ad-hoc-network-based ADAS, for example,may employ GPS and mapping data in conjunction with multi-hop geocastV2V and V2I data exchanges to facilitate automated vehicle maneuveringand powertrain control. During assisted and unassisted vehicleoperation, the resident navigation system may identify a recommendedtravel route based on an estimated shortest travel time or estimatedshortest travel distance between route origin and route destination fora given trip. This recommended travel route may then be displayed as amap trace or as turn-by-turn driving directions on a geocoded andannotated map with optional voice commands output by the in-vehicleaudio system.

Automated and autonomous vehicle systems may employ an assortment ofcommercially available components to provide target object detection andranging. In one example, radio detection and ranging (RADAR) systemsdetect the presence, distance, and/or speed of a target object bydischarging pulses of high-frequency electromagnetic waves that arereflected off the object back to a suitable radio receiver. As anotheroption, the vehicle may be furnished with a laser detection and ranging(LADAR) elastic backscatter system that emits and detects pulsed laserbeams to make precise distance measurements. Synonymous to—and oftenused as the umbrella term for—LADAR-based detection is light detectionand ranging (LIDAR) technology that determines distances to stationaryor moving target objects using assorted forms of light energy, includinginvisible, infrared light spectrums (around 300-700 nanometers) andnear-infrared laser light spectrums (around 900-1100 nanometers). In allthree techniques, the range to an object is determined by measuring thetime delay between transmission of a pulse and detection of thereflected signal.

SUMMARY

Disclosed herein are target object detection systems with attendantcontrol logic for deriving vehicle speed and trajectory, methods formaking and methods for operating such object detection systems, andintelligent motor vehicles with vehicle speed and trajectory estimationcapabilities. By way of example, there are presented wide aperture radarsystems and adaptive logic for accurately estimating velocity vectors ofhost vehicles. These systems and methods derive host vehicle velocity bydemarcating between static and moving objects in proximity to the hostvehicle and estimating the relative velocities of the static objects. Aresident multiple input, multiple output (MIMO) radar system with wideaperture transceivers detects objects within proximity to the hostvehicle and estimates reflection points and relative velocity vectorsfor these objects. The control system classifies a majority of theobjects in proximity to the vehicle as static, and construes staticobjects as those having a relative spatial spread in angle and range.Each target object is categorized as either stationary or moving byidentifying as static a majority of objects that are grouped inside avelocity vector cluster with a predetermined spatial spread in angle andrange. A host vehicle velocity vector is estimated from an average ofthe relative velocities of the static objects within a select one of thevelocity vector clusters.

Attendant benefits for at least some of the disclosed concepts includeaccurate ego-motion estimation for enhanced autonomous driving throughimproved host vehicle localization, object detection and tracking (e.g.,occupancy grid mapping), and target object classification. As usedherein, the term “ego-motion” may be defined to include themulti-directional motion of an object tracking system within athree-dimensional (3D) environment. In addition to improving hostvehicle velocity estimation and target object tracking, disclosedfeatures also help to extend CAS/ACC/FSRACC functionality to disparateroadway topographies and continuously changing driving scenarios. ADASand self-driving vehicle frameworks implementing disclosed objecttracking and velocity estimation techniques help to enhance passengercomfort while minimizing risk of collision. Enhanced host vehiclevelocity analysis also helps to ensure top-level automated drivingperformance, yielding more consistent and reliable system operation,without requiring the addition of dedicated sensors and hardware.

Aspects of this disclosure are directed to methods for making andmethods for using any of the disclosed motor vehicles, automated drivingsystems, and/or target object detection devices. In an example, a methodis presented for governing operation of a motor vehicle. Thisrepresentative method includes, in any order and in any combination withany of the above and below disclosed options and features: emitting oneor more electromagnetic signals via an electronic transmitter (e.g., acontinuous wave or pulse-Doppler radio transmitter) of a target objectdetection system of the motor vehicle; receiving, via an electronicreceiver (e.g., a wide aperture antenna array) of the target objectdetection system, multiple reflection echoes caused by eachelectromagnetic signal reflecting off of multiple target objects withinproximity of the motor vehicle; determining, via a resident or remotevehicle controller based on the received reflection echoes, a relativevelocity vector for each target object; the vehicle controller assigningthe relative velocity vectors to discrete velocity vector clusters, eachof which has a respective centroid and a respective spatial spread ofthe velocity vectors in that cluster to the centroid; estimating a hostvehicle velocity vector of the motor vehicle as an average of therelative velocity vectors in a select velocity vector cluster containingthe most relative velocity vectors and having the largest spatialspread; and the vehicle controller transmitting one or more commandsignals to one or more resident vehicle systems to execute one or morecontrol operations responsive to the estimated host vehicle velocityvector.

Additional aspects of this disclosure are directed to motor vehicleswith host vehicle velocity vector estimation capabilities. As usedherein, the terms “vehicle” and “motor vehicle” may be usedinterchangeably and synonymously to include any relevant vehicleplatform, such as passenger vehicles (e.g., combustion engine, hybrid,full electric, fully and partially autonomous, etc.), commercialvehicles, industrial vehicles, tracked vehicles, off-road andall-terrain vehicles (ATV), motorcycles, farm equipment, watercraft,aircraft, etc. For purposes of this disclosure, the terms “automated”and “autonomous” may be used synonymously and interchangeably to includevehicles and vehicle systems provisioning assisted and/or fullyautonomous driving capabilities, including vehicle platforms classifiedas a Society of Automotive Engineers (SAE) Level 2, 3, 4 or 5 vehicle.SAE Level 2, for example, allows for unassisted and partially assisteddriving with sufficient automation for limited vehicle control, such asauto steer and full-speed range active cruise control (FSRACC), whileobliging immediate driver intervention. At the upper end of the spectrumis Level 5 automation that altogether eliminates human intervention fromvehicle driving operation, e.g., eliminating the steering wheel,throttle and brake pedals, shift knob, etc.

In an example, a motor vehicle is presented that includes a vehicle bodywith multiple road wheels and other standard original equipment. A primemover, which may be embodied as an internal combustion engine (ICE)assembly and/or an electric traction motor, is mounted to the vehiclebody and operates to drive one or more of the road wheels to therebypropel the vehicle. A target object detection system, which may beembodied as a wide aperture radar system operating along or inconjunction with another suitable object detection technology, is alsomounted to the vehicle body. The target object detection system includesan electronic transmitter and receiver, which may be embodied as asingle electromagnetic transceiver unit or as discrete transmitter andreceiver devices.

Continuing with the above example, the motor vehicle also includes avehicle controller, which may be embodied as an electronic control unitor a network of distributed controllers or control modules that regulateoperation of one or more resident vehicle systems. The vehiclecontroller is programmed to command the electronic transmitter to emitan electromagnetic signal; the electronic receiver receives multiplereflection echoes caused by the electromagnetic signal reflecting off oftarget objects within proximity of the motor vehicle. Based on thesereceived reflection echoes, the controller determines a relativevelocity vector for each target object, and assigns the relativevelocity vectors to discrete velocity vector clusters. A host vehiclevelocity vector of the motor vehicle is estimated as an average of therelative velocity vectors in a select one of the velocity vectorclusters that contains the most relative velocity vectors and has thelargest spatial spread. The vehicle controller then commands at leastone resident vehicle system to execute one or more control operationsbased on the estimated host vehicle velocity vector.

Additional aspects of the present disclosure are directed to techniques,algorithms, and logic for operating or manufacturing any of thedisclosed vehicles, systems and devices. Aspects of the presentdisclosure are also directed to target object detection systems andautomated or autonomous control systems for governing operation ofvehicle systems. Also presented herein are non-transitory, computerreadable media storing instructions executable by at least one of one ormore processors of one or more programmable control units, such as anelectronic control unit (ECU) or control module, to govern operation ofa disclosed vehicle, system or device.

The above summary is not intended to represent every embodiment or everyaspect of the present disclosure. Rather, the foregoing summary merelyprovides an exemplification of some of the novel concepts and featuresset forth herein. The above features and advantages, and other featuresand attendant advantages of this disclosure, will be readily apparentfrom the following detailed description of illustrated examples andrepresentative modes for carrying out the present disclosure when takenin connection with the accompanying drawings and the appended claims.Moreover, this disclosure expressly includes any and all combinationsand subcombinations of the elements and features presented above andbelow.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a partially schematic, side-view illustration of arepresentative motor vehicle with a network of in-vehicle controllers,sensing devices, and communication devices for executing automateddriving operations in accordance with aspects of the present disclosure.

FIG. 2 is a diagrammatic illustration of a representative occupancy gridmap showing a host vehicle with a wide aperture radar systemprovisioning target object tracking and velocity vector estimation inaccord with aspects of the disclosed concepts.

FIG. 3 is a graph of target object relative velocities showing therespective relative velocity vectors of the target objects of FIG. 3grouped into discrete vector clusters in accord with aspects of thedisclosed concepts.

FIG. 4 is a flowchart illustrating a representative target objecttracking and ego-motion estimation protocol for deriving host vehiclevelocity vectors, which may correspond to memory-stored instructionsexecuted by an onboard or remote controller, control-logic circuitry,programmable electronic control unit, or other integrated circuit (IC)device or a network of IC devices in accord with aspects of thedisclosed concepts.

The present disclosure is amenable to various modifications andalternative forms, and some representative embodiments are shown by wayof example in the drawings and will be described in detail herein. Itshould be understood, however, that the novel aspects of this disclosureare not limited to the particular forms illustrated in theabove-enumerated drawings. Rather, the disclosure is to cover allmodifications, equivalents, combinations, subcombinations, permutations,groupings, and alternatives falling within the scope of this disclosureas encompassed by the appended claims.

DETAILED DESCRIPTION

This disclosure is susceptible of embodiment in many different forms.Representative embodiments of the disclosure are shown in the drawingsand will herein be described in detail with the understanding that theseembodiments are provided as an exemplification of the disclosedprinciples, not limitations of the broad aspects of the disclosure. Tothat extent, elements and limitations that are described, for example,in the Abstract, Introduction, Summary, and Detailed Descriptionsections, but not explicitly set forth in the claims, should not beincorporated into the claims, singly or collectively, by implication,inference or otherwise.

For purposes of the present detailed description, unless specificallydisclaimed: the singular includes the plural and vice versa; the words“and” and “or” shall be both conjunctive and disjunctive; the words“any” and “all” shall both mean “any and all”; and the words“including,” “containing,” “comprising,” “having,” and the like, shalleach mean “including without limitation.” Moreover, words ofapproximation, such as “about,” “almost,” “substantially,” “generally,”“approximately,” and the like, may each be used herein in the sense of“at, near, or nearly at,” or “within 0-5% of,” or “within acceptablemanufacturing tolerances,” or any logical combination thereof, forexample. Lastly, directional adjectives and adverbs, such as fore, aft,inboard, outboard, starboard, port, vertical, horizontal, upward,downward, front, back, left, right, etc., may be with respect to a motorvehicle, such as a forward driving direction of a motor vehicle when thevehicle is operatively oriented on a normal driving surface.

Referring now to the drawings, wherein like reference numbers refer tolike features throughout the several views, there is shown in FIG. 1 arepresentative automobile, which is designated generally at 10 andportrayed herein for purposes of discussion as a sedan-style passengervehicle. Packaged on a vehicle body 12 of the automobile 10, e.g.,distributed throughout the different vehicle compartments, is an onboardnetwork of electronic devices for executing one or more automated orautonomous driving operations. The illustrated automobile 10—alsoreferred to herein as “motor vehicle” or “vehicle” for short—is merelyan exemplary application with which aspects and features of thisdisclosure may be practiced. In the same vein, implementation of thepresent concepts for the specific autonomous driving systems andoperations discussed below should also be appreciated as exemplaryapplications of novel features disclosed herein. As such, it will beunderstood that aspects and features of this disclosure may be appliedto other automated driving system architectures, utilized for otherautomated driving operations, and implemented for any logically relevanttype of motor vehicle. Moreover, only select components of the automateddriving systems and vehicles are shown and will be described inadditional detail herein. Nevertheless, the vehicles and systemarchitectures discussed herein may include numerous additional andalternative features, and other available peripheral components, forexample, for carrying out the various methods and functions of thisdisclosure.

The representative vehicle 10 of FIG. 1 is originally equipped with avehicle telecommunication and information (“telematics”) unit 14 thatwirelessly communicates (e.g., via cell towers, base stations, mobileswitching centers, satellite service, etc.) with a remotely located or“off-board” cloud computing system 24. Some of the other vehiclehardware components 16 shown generally in FIG. 1 include, asnon-limiting examples, an electronic video display device 18, amicrophone 28, one or more audio speakers 30, and assorted inputcontrols 32 (e.g., buttons, knobs, switches, touchpads, keyboards,touchscreens, etc.). Generally, these hardware components 16 function,in part, as a human/machine interface (HMI) to enable a user tocommunicate with the telematics unit 14 and other systems and systemcomponents within the vehicle 10. Microphone 28 provides a vehicleoccupant with means to input verbal or other auditory commands; thevehicle 10 may be equipped with an embedded voice-processing unitutilizing audio filtering, editing, and analysis software modules.Conversely, speaker 30 provides audible output to a vehicle occupant andmay be either a stand-alone speaker dedicated for use with thetelematics unit 14 or may be part of audio system 22. The audio system22 is operatively connected to a network connection interface 34 and anaudio bus 20 to receive analog information, rendering it as sound, viaone or more speaker components.

Communicatively coupled to the telematics unit 14 is a networkconnection interface 34, suitable examples of which include twistedpair/fiber optic Ethernet switch, internal/external parallel/serialcommunication bus, a local area network (LAN) interface, a controllerarea network (CAN), a media-oriented system transfer (MOST), a localinterconnection network (LIN) interface, and the like. Other appropriatecommunication interfaces may include those that conform with ISO, SAE,and IEEE standards and specifications. The network connection interface34 enables the vehicle hardware 16 to send and receive signals with eachother and with various systems and subsystems both within or “resident”to the vehicle body 12 and outside or “remote” from the vehicle body 12.This allows the vehicle 10 to perform various vehicle functions, such ascontrolling vehicle steering, governing operation of the vehicle'stransmission, controlling engine throttle, engaging/disengaging thebrake system, and other automated driving functions. For instance,telematics unit 14 receives and/or transmits data to/from an autonomoussystems control module (ACM) 52, an engine control module (ECM) 54, apowertrain control module (PCM) 56, sensor interface module(s) 58, abrake system control module (BSCM) 60, and assorted other vehicle ECUs,such as a transmission control module (TCM), a climate control module(CCM), etc.

With continuing reference to FIG. 1, telematics unit 14 is an onboardcomputing device that provides a mixture of services, both individuallyand through its communication with other networked devices. Thistelematics unit 14 is generally composed of one or more processors 40,each of which may be embodied as a discrete microprocessor, anapplication specific integrated circuit (ASIC), or a dedicated controlmodule. Vehicle 10 may offer centralized vehicle control via a centralprocessing unit (CPU) 36 that is operatively coupled to one or moreelectronic memory devices 38, each of which may take on the form of aCD-ROM, magnetic disk, IC device, semiconductor memory (e.g., varioustypes of RAM or ROM), etc., and a real-time clock (RTC) 42. Long-rangevehicle communication capabilities with remote, off-board networkeddevices may be provided via one or more or all of a cellularchipset/component, a navigation and location chipset/component (e.g.,global positioning system (GPS) transceiver), or a wireless modem, allof which are collectively represented at 44. Close-range wirelessconnectivity may be provided via a short-range wireless communicationdevice 46 (e.g., a BLUETOOTH® unit or near field communications (NFC)transceiver), a dedicated short-range communications (DSRC) component48, and/or a dual antenna 50. It should be understood that the vehicle10 may be implemented without one or more of the above listedcomponents, or may include additional components and functionality asdesired for a particular end use. The various communication devicesdescribed above may be configured to exchange data as part of a periodicbroadcast in a V2V communication system or a vehicle-to-everything (V2X)communication system, e.g., Vehicle-to-Infrastructure (V2I),Vehicle-to-Pedestrian (V2P), and/or Vehicle-to-Device (V2D).

CPU 36 receives sensor data from one or more sensing devices that use,for example, photo detection, radar, laser, ultrasonic, optical,infrared, or other suitable technology for executing an automateddriving operation, including short range communications technologiessuch as DSRC or Ultra-Wide Band (UWB). In accord with the illustratedexample, the automobile 10 may be equipped with one or more digitalcameras 62, one or more range sensors 64, one or more vehicle speedsensors 66, one or more vehicle dynamics sensors 68, and any requisitefiltering, classification, fusion and analysis hardware and software forprocessing raw sensor data. The type, placement, number, andinteroperability of the distributed array of in-vehicle sensors may beadapted, singly or collectively, to a given vehicle platform forachieving a desired level of autonomous vehicle operation.

Digital camera 62 may use a charge coupled device (CCD) sensor or othersuitable optical sensor to generate images indicating a field-of-view ofthe vehicle 10, and may be configured for continuous image generation,e.g., at least about 35 images generated per second. By way ofcomparison, range sensor 64 may emit and detect reflected radio,infrared, light-based or other electromagnetic signals (e.g., radar, EMinductive, Light Detection and Ranging (LIDAR), etc.) to detect, forexample, presence, geometric dimensions, and/or proximity of an object.Vehicle speed sensor 66 may take on various forms, including wheel speedsensors that measure wheel speeds, which are then used to determinereal-time vehicle speed. In addition, the vehicle dynamics sensor 68 maybe in the nature of a single-axis or a triple-axis accelerometer, anangular rate sensor, an inclinometer, etc., for detecting longitudinaland lateral acceleration, yaw, roll, and/or pitch rates, or otherdynamics related parameter. Using data from the sensing devices 62, 64,66, 68, the CPU 36 identifies surrounding driving conditions, determinescharacteristics of road surface conditions, identifies objects within adetectable range of the vehicle 10, determines attributes of the targetobject, such as size, relative position, angle of approach, relativespeed, etc., and executes automated control maneuvers based on theseexecuted operations.

These sensors are distributed throughout the motor vehicle 10 inoperatively unobstructed positions relative to views fore and aft or onport and starboard sides of the vehicle. Each sensor generateselectrical signals indicative of a characteristic or condition of atargeted object, generally as an estimate with a corresponding standarddeviation. While the operating characteristics of these sensors aregenerally complementary, some are more reliable in estimating certainparameters than others. Most sensors have different operating ranges andareas of coverage, and are capable of detecting different parameterswithin their operating range. For instance, a radar-based sensor mayestimate range, range rate, and azimuth location of an object, but maynot be robust in estimating the extent of a detected object. Cameraswith optics processing, on the other hand, may be more robust inestimating a shape and azimuth position of an object, but may be lessefficient at estimating the range and range rate of the object. Ascanning-type LIDAR-based sensor may perform efficiently and accuratelywith respect to estimating range and azimuth position, but may be unableto accurately estimate range rate and, thus, may not be accurate withrespect to new object acquisition/recognition. Ultrasonic sensors, bycomparison, are capable of estimating range but are generally unable toaccurately estimate range rate and azimuth position. Further, theperformance of many sensor technologies may be affected by differingenvironmental conditions. Consequently, sensors generally presentparametric variances whose operative overlap offer opportunities forsensory fusion.

FIG. 2 diagrammatically illustrates an occupancy grid map 100 showing ahost vehicle 110, which is equipped with a wide aperture radar system112, traveling through an exemplary driving environment whileprovisioning target object tracking and host vehicle velocity vectorestimation. Although differing in appearance, the vehicle 110 of FIG. 2may take on any of the options and alternatives described above withrespect to the motor vehicle 10 of FIG. 1, and vice versa. Dispersedthroughout the surrounding driving environment of the host vehicle 110is an assortment of stationary objects, such as buildings 101A-101D andparked vehicles 103A-103F, and an assortment of moving objects, such asthe three automobiles 105A-105C traveling through a roadway intersectionforward of the host vehicle 110. Clearly, the driving environmentthrough which host vehicle 110 travels may comprise any number,combination and arrangement of static and moving objects, includingurban and rural settings, highway and roadway driving, pedestrian-denseand vehicle-only scenarios, etc.

As the host vehicle 110 travels through the driving environment, anelectronic “impulse” radio wave transmitter 114 of the radar system 112radiates electromagnetic signals, namely W-band radio wave pulses with afrequency range of approximately 70-120 GHz with a modulated pulserepetition frequency (PRF). After the transmitted radar signals contactone or more of the objects 101A-101D, 103A-103F, and 105A-105C on ornear the road across which the vehicle 10 is travelling, they arereflected/redirected back towards the radar system 112. Thesereflected/redirected radar signals are received by an electronic radioreceiver, such as first and second wide aperture antenna arrays 116A and116B. The transmitter 114 and receivers 116A, 116B are portrayed in FIG.2 as discrete components; however, it is anticipated that they becombined into a unitary, steerable radio transceiver array in which anantenna operates as both transmitter and receiver.

The stream of impulses radiated by the radio wave transmitter 114, whosebeam direction may be governed by a suitable actuator and beam steeringmodule, are reflected by target objects and background clutter andreceived by the antenna arrays 116A and 116B, each of which may providea common wide aperture for transmission and reception. Received signalsmay then be amplified via a wideband amplifier before being passed ontoa digital signal processor that carries out analog-to-digitalconversion, velocity filtering, data fusion, coherent integration, andmotion compensation of the impulse signals. Processed signals outputfrom the digital signal processor may thereafter be passed to a targetpost processor, where target parameters such as target range, relativetarget speed and trajectory, target angle of attack, and target trackingand identification are determined. Digitized samples are thereafterplaced into complementary bit locations of a random-access memory (RAM)capable of receiving inputs, e.g., at an eight-nanosecond rate (˜125MSPS). In at least some disclosed wide-aperture radar systems,sub-aperture processing may be employed to facilitate a long coherentprocessing interval (CPI) or to implement wide-swath mode imaging.

While it is envisioned that target object detection systems and hostvehicle velocity estimation techniques set forth herein may beimplemented through any suitable target object detection technique,including LIDAR, LADAR, short aperture radar, and the like, manydisclosed features are most effective by way of implementation throughwide aperture radar systems. Wide-Aperture Synthetic Aperture Radar(WASAR) may be typified as a radar system employing a synthesizedaperture whose angular extent exceeds the sector required for equalresolution in range and cross-range (e.g., bandwidth of approximately75-80 GHz with an aperture size of approximately 40-50 cm). A very largeaperture may be computationally synthesized using data recorded from theradar echoes of a series of radar transmissions. This large syntheticaperture provides fine resolution in the direction of motion while alarge bandwidth provides fine resolution in range. For instance, wideaperture radar systems with wide aperture antenna arrays with widelyspaced antenna elements, such as spacing larger than half a wavelength,may provision high-resolution imagery at a relatively low cost. Anotheradvantage of utilizing multiple antenna arrays with widely spacedantenna elements is the ability to eliminate ghost targets. Multipleantenna arrays which utilize such antenna spacing can be used withmultiple radars since any attendant ambiguities in the anglemeasurements due to the widely spaced elements can be resolved byintersecting the grating lobes from multiple radars. A wideband signal,with its inherent high-range resolution, provides the capability toresolve individual scattering centers of a complex target and therebyproduce a target's unique radar signature. The use of a filter matchedto the expected received waveform can result in enhanced detectioncapability, target identification, and discrimination of unwanted falsetargets.

With reference now to the flow chart of FIG. 4, an improved method orcontrol strategy for tracking target objects, such as objects 101A-101D,103A-103F, and 105A-105C of FIG. 2, and deriving velocity vectors ofhost vehicles, such as vehicles 10 and 110 of FIGS. 1 and 2, isgenerally described at 200 in accordance with aspects of the presentdisclosure. Some or all of the operations illustrated in FIG. 4 anddescribed in further detail below may be representative of an algorithmthat corresponds to processor-executable instructions that may bestored, for example, in main or auxiliary or remote memory, andexecuted, for example, by an on-board or off-board controller,processing unit, control logic circuit, or other module or device ornetwork of modules/devices, to perform any or all of the above or belowdescribed functions associated with the disclosed concepts. It should berecognized that the order of execution of the illustrated operationblocks may be changed, additional blocks may be added, and some of theblocks described may be modified, combined, or eliminated.

Method 200 begins at terminal block 201 of FIG. 4 withprocessor-executable instructions for a programmable controller orcontrol module or similarly suitable processor to call up aninitialization procedure for a target object detection protocol. Thisroutine may be executed in real-time, continuously, systematically,sporadically, and/or at regular intervals, for example, each 100milliseconds, during active or autonomous vehicle operation. As yetanother option, block 201 may initialize responsive to a user promptfrom an occupant of the vehicle or a broadcast prompt signal from abackend or middleware computing node tasked with collecting, analyzing,sorting, storing and distributing vehicle data. To carry out thisprotocol, a vehicle control system or any combination of one or moresubsystems may be operable to receive, process, and synthesize pertinentinformation and inputs, and execute control logic and algorithms toregulate various powertrain system, steering system, brake system, fuelsystem, and/or battery system components to achieve desired controltargets.

As part of the initialization procedure at block 201, for example, aresident vehicle telematics unit or other similarly suitable HumanMachine Interface (HMI) may execute a navigation processing codesegment, e.g., to obtain vehicle data (e.g., geospatial data, speed,heading, acceleration, timestamp, etc.), and optionally display selectaspects of this data to an occupant of the vehicle 10, 110, e.g., viavideo display device 18 of telematics unit 14. The occupant may employany of the HMI input controls 32 to then select a desired destinationand, optionally, one or more detour stops for the vehicle from a currentlocation or an expected origin. Path plan data may be generated with apredicted path for the vehicle to travel from origin to destination. Itis also envisioned that the ECU or telematics unit processors receivevehicle origin and vehicle destination information from other sources,such as a server-class computer provisioning data exchanges for a cloudcomputing system or a dedicated mobile software application operating ona smartphone or other handheld computing device.

Upon initialization of the control protocol at block 201, method 200proceeds to process block 203 with memory-stored, processor-executableinstructions to identify and group target object detection points intodiscrete object clusters. As indicated above, an electronic transmitterof a host vehicle's target object detection system discharges anelectromagnetic signal as the vehicle travels along a desired route. Anelectronic receiver of the target object detection system senses signalechoes caused by the electromagnetic signal reflecting off of targetobjects within proximity of the host vehicle. Due to variations intarget size, geometry, and orientation, each target object may engenderseveral detection points, each of which produces a resultant reflectionecho. FIG. 2 depicts a driving environment wherein the target objects101A-101D, 103A-103F, and 105A-105C are displayed with target detectionpoints resulting from a number of radar cycles. By way of non-limitingexample, each static buildings 101A-101D has between five and sevendetection points, illustrated in FIG. 2 as empty circles located onfacing surfaces of the building structures. Comparatively, the stoppedand traveling vehicles 103A-103F and 105A-105C, respectively, each hasbetween three and four detection points, also portrayed as emptycircles. Detected targets may propagate numerous detection points due tothe large size of the object with respect to the radar's spatialresolution.

The ability to accurately pinpoint and classify objects may be partiallydependent on the observation interval and signal pulse modulation; assuch, static clutter can oftentimes be localized more accurately thanmoving targets. The ability to accurately detect and localize staticclutter may be exploited to “clean up” a cluttered scene in order toimprove target object detection and localization. At process block 203,all of the detection points within a given prediction time interval fora given route are grouped into discrete object clusters (indicated byhidden circles 107A-107M in FIG. 2) based on the relative proximitiesand/or relative velocities of the detection points with respect to oneanother. The detection points may be deemed “proximal” or “adjacent” iftheir mapped locations (e.g., radar estimates of individual object'sposition (range, azimuth, elevation) with respect to the radar'sposition) are within a predetermined proximity to one another. Asanother option, the method 200 may be operative to assemble detectionpoints into object clusters further in response to the velocity vectorsof the detection points being substantially equal. For example, if asubset of perceived detection points are proximate to each other andhave similar velocities, it may be assumed that these detections are allon the same object. Concomitantly, this cluster of detection points maybe saved in cache memory as a single object for tracking purposes andfuture reference.

With continuing reference to FIG. 4, the method 200 estimates arespective relative velocity vector for each object cluster, asindicated by predefined process block 205, and contemporaneouslyestimates the position of cluster centroid for each object cluster, asindicated by process block 207. Cluster centroid position may beestimated by taking the mathematical average of all or a select subsetof the respective locations of the reflection points in the subjectobject cluster. FIG. 2 illustrates thirteen (13) cluster centroids, onefor each target object 101A-101D, 103A-103F, and 105A-105C; thesecluster centroids are depicted as solid circles within the objectclusters 107A-107M. In at least some embodiments, determining therelative velocity vector for a subject object may encompass estimating avelocity vector for the cluster centroid of the object clustercorresponding to that target object. In this regard, the velocity vectorfor an object cluster is calculated relative to a correspondingposition, speed and trajectory of the host vehicle at the time ofevaluation. FIG. 2 illustrates thirteen (13) relative velocity vectors,one for each object cluster 107A-107M; each relative velocity vector isdepicted as a solid arrow projecting from a related centroid.

Continuing with the foregoing discussion, each of the reflection echoesmay be delineated into interrelated echo projections, such as first andsecond echo projections 109A and 109B in FIG. 2, resulting from multipleantenna arrays 116A, 116B detecting a single radar pulse output byimpulse radio wave transmitter 114. An estimated Dopplerfrequency—detection point Doppler frequency vector f (shown below)—andan estimated angle of arrival—detection point azimuth angle matrix H(shown below)—are derived for each reflection echo based on itscorresponding echo projections:

${f = \begin{bmatrix}f_{1} \\f_{2} \\\vdots\end{bmatrix}};{H = \begin{bmatrix}{\sin\left( \theta_{1} \right)} & {\cos\left( \theta_{1} \right)} \\{\sin\left( \theta_{2} \right)} & {\cos\left( \theta_{2} \right)} \\\vdots & \vdots\end{bmatrix}}$where the relation of Doppler frequency to azimuth angle and velocityvector may be characterized as:

$\begin{bmatrix}f_{1} \\f_{2} \\\vdots\end{bmatrix} = {\begin{bmatrix}{\sin\left( \theta_{1} \right)} & {\cos\left( \theta_{1} \right)} \\{\sin\left( \theta_{2} \right)} & {\cos\left( \theta_{2} \right)} \\\vdots & \vdots\end{bmatrix}\begin{bmatrix}v_{x} \\v_{y}\end{bmatrix}}$where f_(i) is a Doppler frequency of the i^(th) detection point; θ_(i)is an angle of the i^(th) detection point; and v_(x) and v_(y) arevector coordinates indicative of the estimated velocity vector. Usingthis data, the relative velocity vector is calculated as an estimatedvelocity vector {circumflex over (v)} as follows:{circumflex over (v)}=(H ^(T) H+σI)⁻¹ H ^(T) fwhere H^(T) is a transpose of the azimuth angle matrix H; a is aregularization factor; and I is an inversion factor. Regularizationfactor σ helps to provide numerical stabilization for inversion of thematrix H^(T)·H, e.g., for scenarios in which that matrix has an illcondition.

Once the object detection points are identified and clustered (block203), the object cluster velocity vectors are estimated (block 205), andthe cluster centroids are located (block 207), the method 200 advancesto process block 209 and groups the velocity vectors into discretevector clusters. FIG. 3, for example, is a Cartesian graph of targetobject relative velocities (in miles per hour (mph)) with longitudinalvelocity (V_(x)) on the x-axis and lateral velocity (V_(y)) on they-axis. The relative velocity vectors for the target objects 101A-101D,103A-103F, and 105A-105C of FIG. 2 are broken down into their respectivelateral and longitudinal vector components, and then mapped to therelative velocity graph 120 based on a respective velocity coordinatesets (V_(x), V_(y)). After all of target objects' velocity vectors aremapped, it can be seen that the ten (10) static objects tend tocongregate around a common area, e.g., on the lower right-hand side ofthe graph 120, whereas the three (3) moving objects tend to be dispersedto isolated areas, e.g., on the upper left and right-hand sides of thegraph 120. The method 200 proceeds to generate discrete vectorclusters—labelled 120A-120C in FIG. 3—as a function of the spatialspread between proximal ones of the velocity coordinate sets on thevelocity graph 120. Each vector cluster 120A-120C has a respectivecentroid and a respective spatial spread, such as static vector clustercentroid 121 and spatial spread 123.

Predefined process block 211 calculates a respective ego velocity scorefor each vector cluster based, at least in part, on the total number ofrelative velocity vectors contained in that vector cluster, a varianceof detections of the spatial position of the centroid associated witheach relative velocity vector in that vector cluster, and the proximityof the estimated vehicle velocity to a prior course ego velocityestimate. At input/output block 213, prior-course ego velocityestimation data is received/retrieved, e.g., from resident memorydevices 38 or remote cloud computing system 24. Prior-course egovelocity estimate data may optionally be embodied as an input to theADAS system from a vehicle dynamics sensor or from vehicle audiometrymeasurements. The ego velocity score for each of vector cluster may thenbe calculated as:

$S = {{\alpha\; N} + {\beta\frac{1}{N}{\sum\limits_{n = 1}^{N}{{p_{i} - {\frac{1}{N}{\sum\limits_{n = 1}^{N}p_{i}}}}}^{2}}} - {\gamma{{\nu_{pr} - {\frac{1}{N}{\sum\limits_{n = 1}^{N}\nu_{i}}}}}^{2}}}$where S is the ego velocity score; N is the number of the relativevelocity vectors in the vector cluster; v_(i) is an i^(th) velocityvector in the vector cluster; p_(i) is a centroid position of detectionpoints associated with the i^(th) velocity vector; v_(pr) is a priorcourse estimation of ego-velocity; α and γ and β are weighting factors.The vector clusters 120A-120B may then be ranked in a hierarchicalmanner from highest ego score to lowest ego score.

Advancing from predefined process block 211 to predefined process block215, the method 200 of FIG. 4 estimates a vehicle velocity vector of thehost vehicle. A host vehicle's velocity vector may be calculated as amathematical average of the relative velocity vectors contained in thevector cluster having the highest ego score, namely the clustercontaining the most relative velocity vectors and having the largestspatial spread. The average of the relative velocity vectors may becalculated as a standard mean:

$\mu = {\sum\limits_{n = 1}^{N}\nu_{i}}$a weighted mean:

$\mu = {\sum\limits_{n = 1}^{N}{w_{i}\nu_{i}}}$or a medianMedian(v ₀ ,v ₁ , . . . ,v _(N))of the relative velocity vectors in the selected vector cluster with thehighest ego score. The weight factor w_(i) may be proportional to aconfidence of the velocity that it multiplies; the confidence may beobtained, for example, by the SNR of the detection points that areassociated with the estimation of the particular velocity (v_(i))(higher SNR indicates higher confidence in accuracy of the velocityestimation).

Process block 217 is accompanied by instructions to transmit one or morecommand signals to a resident vehicle subsystem, such as residentvehicle navigation or brake and steering, to execute one or more controloperations associated with the estimated host vehicle velocity vector.The resident vehicle subsystem may be embodied as an autonomous drivingcontrol module that is operable to automate driving of the motorvehicle. In this instance, the vehicle control operation may includeautomating driving of the motor vehicle (e.g., steering, throttle,braking, headway, maneuvering, etc.) to complete a selected predictedroute. As a further option, the resident vehicle subsystem may beembodied as an ADAS control module that is operable to automate selectdriving operations of the vehicle. In this instance, the controloperation may include executing a controller-automated driving maneuverto complete at least a portion of a selected route based on the hostvehicle's velocity vector. As another option, the resident vehiclesubsystem may be embodied as a resident vehicle navigation system withelectronic input and display devices. In this instance, the vehiclecontrol operation may include the display device displaying a predictedroute contemporaneous with the estimated ego velocity. The input devicemay receive a user command to determine an alternative route for drivingthe motor vehicle. At this juncture, the method 200 of FIG. 4 mayadvance from process block 217 to terminal block 219 and terminate, ormay loop back to terminal block 201 and run in a continuous loop.

Aspects of this disclosure may be implemented, in some embodiments,through a computer-executable program of instructions, such as programmodules, generally referred to as software applications or applicationprograms executed by any of a controller or the controller variationsdescribed herein. Software may include, in non-limiting examples,routines, programs, objects, components, and data structures thatperform particular tasks or implement particular data types. Thesoftware may form an interface to allow a computer to react according toa source of input. The software may also cooperate with other codesegments to initiate a variety of tasks in response to data received inconjunction with the source of the received data. The software may bestored on any of a variety of memory media, such as CD-ROM, magneticdisk, bubble memory, and semiconductor memory (e.g., various types ofRAM or ROM).

Moreover, aspects of the present disclosure may be practiced with avariety of computer-system and computer-network configurations,including multiprocessor systems, microprocessor-based orprogrammable-consumer electronics, minicomputers, mainframe computers,and the like. In addition, aspects of the present disclosure may bepracticed in distributed-computing environments where tasks areperformed by resident and remote-processing devices that are linkedthrough a communications network. In a distributed-computingenvironment, program modules may be located in both local and remotecomputer-storage media including memory storage devices. Aspects of thepresent disclosure may therefore be implemented in connection withvarious hardware, software or a combination thereof, in a computersystem or other processing system.

Any of the methods described herein may include machine readableinstructions for execution by: (a) a processor, (b) a controller, and/or(c) any other suitable processing device. Any algorithm, software,control logic, protocol or method disclosed herein may be embodied assoftware stored on a tangible medium such as, for example, a flashmemory, a CD-ROM, a floppy disk, a hard drive, a digital versatile disk(DVD), or other memory devices. The entire algorithm, control logic,protocol, or method, and/or parts thereof, may alternatively be executedby a device other than a controller and/or embodied in firmware ordedicated hardware in an available manner (e.g., implemented by anapplication specific integrated circuit (ASIC), a programmable logicdevice (PLD), a field programmable logic device (FPLD), discrete logic,etc.). Further, although specific algorithms are described withreference to flowcharts depicted herein, many other methods forimplementing the example machine-readable instructions may alternativelybe used.

Aspects of the present disclosure have been described in detail withreference to the illustrated embodiments; those skilled in the art willrecognize, however, that many modifications may be made thereto withoutdeparting from the scope of the present disclosure. The presentdisclosure is not limited to the precise construction and compositionsdisclosed herein; any and all modifications, changes, and variationsapparent from the foregoing descriptions are within the scope of thedisclosure as defined by the appended claims. Moreover, the presentconcepts expressly include any and all combinations and subcombinationsof the preceding elements and features.

What is claimed:
 1. A method for controlling automated operations of amotor vehicle, the method comprising: emitting, via an electronictransmitter of a target object detection system of the motor vehicle, anelectromagnetic signal; receiving, via an electronic receiver of thetarget object detection system, multiple reflection echoes caused by theelectromagnetic signal reflecting off of multiple target objects withinproximity of the motor vehicle; determining, via a vehicle controllerbased on the received reflection echoes, a relative velocity vector foreach of the target objects; assigning the relative velocity vectors todiscrete velocity vector clusters, each of the velocity vector clustershaving a respective centroid and a respective spatial spread of therelative velocity vectors in the velocity vector cluster to thecentroid; estimating a host vehicle velocity vector of the motor vehicleas an average of the relative velocity vectors in a select one of thevelocity vector clusters containing a majority of the relative velocityvectors and having a largest of the spatial spreads; and transmitting,via the vehicle controller, a command signal to a resident vehiclesystem resident to the motor vehicle to execute a control operationacting on the motor vehicle responsive to the estimated host vehiclevelocity vector.
 2. The method of claim 1, wherein each of the targetobjects includes multiple detection points causing a plurality of thereflection echoes, each of the detection points corresponding to arespective one of the reflection echoes, the method further comprisingidentifying each of the target objects by assigning the detection pointsto discrete object clusters based on relative proximities of thedetection points to one another.
 3. The method of claim 2, furthercomprising determining, for each of the reflection echoes, an estimatedDoppler frequency and an estimated angle of arrival.
 4. The method ofclaim 3, further comprising estimating a respective position of acluster centroid for each of the object clusters, wherein determiningthe relative velocity vector for each of the target objects includesestimating a velocity vector for the cluster centroid from the estimatedangles and Doppler frequencies of the detection points in the objectcluster corresponding to the target object.
 5. The method of claim 4,wherein determining the relative velocity vectors includes calculating,for each of the target objects, an estimated velocity vector {circumflexover (v)} as:{circumflex over (v)}=(H ^(T) H+σI)⁻¹ H ^(T) f where H is an azimuthangle matrix of the detection points; H^(T) is a transpose of theazimuth angle matrix H; σ is a regularization factor; f is a vector ofDoppler frequencies of the detection points; and I is an inversionfactor.
 6. The method of claim 1, further comprising calculating, foreach of the velocity vector clusters, a respective ego velocity scorebased on: a number of the relative velocity vectors in the velocityvector cluster; a variance of detections of a spatial position of acentroid associated with each relative velocity vector in the velocityvector cluster; and a proximity to a prior course ego velocity estimate.7. The method of claim 6, wherein the ego velocity score for each of thevelocity vector clusters is calculated as:$S = {{\alpha\; N} + {\beta\frac{1}{N}{\sum\limits_{n = 1}^{N}{{p_{i} - {\frac{1}{N}{\sum\limits_{n = 1}^{N}p_{i}}}}}^{2}}} - {\gamma{{\nu_{pr} - {\frac{1}{N}{\sum\limits_{n = 1}^{N}\nu_{i}}}}}^{2}}}$where S is the ego velocity score; N is the number of the relativevelocity vectors in the velocity vector cluster; v_(i) is an i^(th)velocity vector in the velocity vector cluster; p_(i) is a centroidposition of detection points associated with the i^(th) velocity vector;v_(pr) is the prior course ego velocity estimate; and α and γ and β areweighting factors.
 8. The method of claim 1, wherein the average of therelative velocity vectors is calculated as a standard mean, a weightedmean, or a median of the relative velocity vectors in the select one ofthe velocity vector clusters.
 9. The method of claim 1, furthercomprising mapping each of the relative velocity vectors to a Cartesianvelocity graph based on a respective velocity coordinate set (V_(x),V_(y)) of the relative velocity vector, wherein assigning the relativevelocity vectors to the velocity vector clusters includes generating thediscrete velocity vector clusters as a function of the spatial spreadbetween proximal ones of the velocity coordinate sets on the Cartesianvelocity graph.
 10. The method of claim 1, wherein the resident vehiclesystem includes an autonomous driving control module operable toautomate driving of the motor vehicle, the control operation includingautomating driving of the motor vehicle to complete a planned routebased on the estimated host vehicle velocity vector.
 11. The method ofclaim 1, wherein the resident vehicle system includes an Advanced DriverAssistance System (ADAS) control module operable to automate control ofthe motor vehicle, the control operation including executing a braking,steering, and/or acceleration maneuver of the motor vehicle based on theestimated host vehicle velocity vector.
 12. The method of claim 1,wherein the resident vehicle system includes a vehicle navigation systemwith a display device mounted inside the motor vehicle, the controloperation including displaying, via the display device, indicators forthe target objects within proximity of the motor vehicle.
 13. The methodof claim 1, wherein the target object detection system includes a wideaperture radar system, the electronic transmitter includes a radio wavetransmitter, and the electronic receiver includes multiple wide apertureantenna arrays.
 14. A motor vehicle comprising: a vehicle body with aplurality of road wheels mounted to the vehicle body; a prime moverattached to the vehicle body and configured to drive one or more of theroad wheels to thereby propel the motor vehicle; a target objectdetection system mounted to the vehicle body and including an electronictransmitter and an electronic receiver; and a vehicle controlleroperatively connected to the target object detection system, the vehiclecontroller being programmed to: command the electronic transmitter toemit an electromagnetic signal; receive, via the electronic receiver,multiple reflection echoes caused by the electromagnetic signalreflecting off of multiple target objects within proximity of the motorvehicle; determine, based on the received reflection echoes, a relativevelocity vector for each of the target objects; assign the relativevelocity vectors to discrete velocity vector clusters, each of thevelocity vector clusters having a respective centroid and a respectivespatial spread of the relative velocity vectors in the velocity vectorcluster to the centroid; estimate a host vehicle velocity vector of themotor vehicle as an average of the relative velocity vectors in a selectone of the velocity vector clusters containing a majority of therelative velocity vectors and having a largest of the spatial spreads;and transmit a command signal to a resident vehicle system resident tothe motor vehicle to execute a control operation acting on the motorvehicle responsive to the estimated host vehicle velocity vector. 15.The motor vehicle of claim 14, wherein each of the target objectsincludes multiple detection points causing a plurality of the reflectionechoes, each of the detection points corresponding to a respective oneof the reflection echoes, the vehicle controller being furtherprogrammed to identify each of the target objects by assigning thedetection points to discrete object clusters based on relativeproximities of the detection points to one another.
 16. The motorvehicle of claim 15, wherein the vehicle controller is furtherprogrammed to determine, for each of the reflection echoes, an estimatedDoppler frequency and an estimated angle of arrival.
 17. The motorvehicle of claim 16, wherein the vehicle controller is furtherprogrammed to estimate a respective position of a cluster centroid foreach of the object clusters, wherein determining the relative velocityvector for each of the target objects includes estimating a velocityvector for the cluster centroid from the estimated angles and Dopplerfrequencies of the detection points in the object cluster correspondingto the target object.
 18. The motor vehicle of claim 14, wherein thevehicle controller is further programmed to calculate, for each of thevelocity vector clusters, a respective ego velocity score based on: anumber of the relative velocity vectors in the velocity vector cluster;a variance of detections of a spatial position of a centroid associatedwith each relative velocity vector in the velocity vector cluster; and aproximity to a prior course ego velocity estimate.
 19. The motor vehicleof claim 14, wherein the average of the relative velocity vectors iscalculated as a standard mean, a weighted mean, or a median of therelative velocity vectors in the select one of the velocity vectorclusters.
 20. The motor vehicle of claim 14, wherein the target objectdetection system includes a wide aperture radar system, the electronictransmitter includes a radio wave transmitter, and the electronicreceiver includes multiple wide aperture antenna arrays.